A brief review of dispensing-based rapid prototyping techniques in tissue scaffold fabrication: role of modeling on scaffold properties prediction
Bibliographic record
Abstract
Artificial scaffolds play vital roles in tissue engineering as they provide a supportive environment for cell attachment, proliferation and differentiation during tissue formation. Fabrication of tissue scaffolds is thus of fundamental importance for tissue engineering. Of the variety of scaffold fabrication techniques available, rapid prototyping (RP) methods have attracted a great deal of attention in recent years. This method can improve conventional scaffold fabrication by controlling scaffold microstructure, incorporating cells into scaffolds and regulating cell distribution. All of these contribute towards the ultimate goal of tissue engineering: functional tissues or organs. Dispensing is typically used in different RP techniques to implement the layer-by-layer fabrication process. This article reviews RP methods in tissue scaffold fabrication, with emphasis on dispensing-based techniques, and analyzes the effects of different process factors on fabrication performance, including flow rate, pore size and porosity, and mechanical cell damage that can occur in the bio-manufacturing process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".